RGBT Tracking

RGBT tracking aims to robustly track objects using both visible (RGB) and thermal infrared (TIR) imagery, leveraging the complementary strengths of each modality to overcome challenges like low light or adverse weather conditions. Current research heavily emphasizes effective multimodal fusion strategies, often employing transformer-based architectures and exploring various fusion levels (pixel, feature, decision) to optimally combine RGB and TIR information. These advancements are improving the accuracy and robustness of object tracking in challenging scenarios, with significant implications for applications such as autonomous driving, surveillance, and robotics.